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Opioids are a class of drugs that mimic endogenous opioid peptides and act on opioid receptors, and help in pain relief. These compounds are classified as natural, synthetic, or semi-synthetic. Natural opioids, like morphine, codeine, and thebaine, are derived from the opium poppy plant (Papaver somniferum or Papaver album) and are termed opiates. Synthetic opioids are artificial, while semi-synthetic opioids combine natural and synthetic compounds. Morphine, a prototypical opioid, possesses a...
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Identifying factors associated with opioid cessation in a biracial sample using machine learning.

Jiayi W Cox1, Richard M Sherva1, Kathryn L Lunetta2

  • 1Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA.

Exploration of Medicine
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Summary

Opioid cessation is influenced by factors like cocaine use, duration of opioid use, and age, with unique predictors for African Americans and European ancestry individuals. Further research can improve opioid use disorder management.

Keywords:
Opioid use disorderfeature selectionmachine learningopioid cessationoutcome prediction

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Area of Science:

  • Addiction research
  • Psychiatry
  • Machine learning in healthcare

Background:

  • Racial disparities in opioid use disorder (OUD) management are significant.
  • Limited research exists on factors influencing opioid cessation across diverse populations.

Purpose of the Study:

  • To identify factors associated with opioid cessation in African Americans (AAs) and individuals of European ancestry (EAs).
  • To compare predictors of opioid cessation between these two demographic groups.

Main Methods:

  • Utilized machine learning algorithms (LASSO, Random Forest, DNN, SVM) on a dataset of 1,192 AAs and 2,557 EAs with OUD.
  • Analyzed nearly 4,000 variables including demographics, substance use, general health, and psychiatric diagnoses.

Main Results:

  • Support Vector Machine models achieved prediction accuracies of 75.4% in AAs and 79.4% in EAs.
  • Key independent predictors for both groups included less recent cocaine use, shorter duration of opioid use, and older age.
  • Specific predictors identified were gambling severity for AAs, and PTSD recovery, antisocial behaviors, and atheism for EAs.

Conclusions:

  • Findings offer a foundation for hypothesis-driven research into OUD management strategies.
  • Identified factors can inform tailored approaches to improve OUD treatment outcomes for diverse populations.